A protein-based machine learning approach to the identification of inflammatory subtypes in pancreatic ductal adenocarcinoma.

ABSTRACT:

Background/objectives

The inherently immunosuppressive tumor microenvironment along with the heterogeneity of pancreatic ductal adenocarcinoma (PDAC) limits the effectiveness of available treatment options and contributes to the disease lethality. Using a machine learning algorithm, we hypothesized that PDAC may be categorized based on its microenvironment inflammatory milieu.

Methods

Fifty-nine tumor samples from patients naïve to treatment were homogenized and probed for 41 unique inflammatory proteins using a multiplex assay. Subtype clustering was determined using t-distributed stochastic neighbor embedding (t-SNE) machine learning analysis of cytokine/chemokine levels. Statistics were performed using Wilcoxon rank sum test and Kaplan-Meier survival analysis.

Results<

SUBMITTER: Herremans KM 

PROVIDER: S-EPMC10528923 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature